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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 44042
Conference: The 22nd International Symposium on Advanced Intelligent Systems (ISIS 2021)

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Prof. Dr. Zong Woo Geem

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Department of Smart City & Energy, Gachon University, Seongnam, Korea
Interests: energy; environment; hydrosystem; renewable energy technologies; optimization; mathematical programming; algorithms; artificial neural networks; harmony search
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Prof. Dr. Seokwon Yeom

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School of AI, Degu University, Gyeongsan 38453, Republic of Korea
Interests: intelligent image processing; deep/machine learning; target tracking
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Prof. Dr. Euntai Kim

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Guest Editor

Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
Interests: pedestrian and vehicle detection; recognition; vision for advanced driver assistance systems (ADAS); robot vision
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Myung-Geun Chun

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Guest Editor

School of Electronics Engineering, Chungbuk National University, Chungbuk 28644, Korea
Interests: feature extraction; discriminant analysis; fuzzy clustering; pattern recognition; face recognition; machine learning; image processing; feature selection; pattern classification; clustering

Prof. Dr. Young-Jae Ryoo

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Guest Editor

Department of Control Engineering and Robotics, Mokpo National University, Jeonnam 58554, Korea
Interests: intelligent systems

Special Issue Information

Dear Colleagues,

This Special Issue invites state-of-the-art research in intelligent systems. It will also include selected papers from the conference of the 22nd International Symposium on Advanced Intelligent Systems (ISIS 2021, http://isis2021.org/), which will be held at CheongJu, South Korea on 15–18 December 2021. The topics of the contributed papers will include various intelligent techniques and their real-world applications.

Prof. Dr. Zong Woo Geem
Prof. Dr. Seokwon Yeom
Prof. Dr. Euntai Kim
Prof. Dr. Myung-Geun Chun
Prof. Dr. Young-Jae Ryoo
Guest Editors

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Keywords

computational intelligence
machine learning
fuzzy logic and reasoning
evolutionary algorithms
soft computing
data mining
big data analysis
probabilistic models and inference
robotics
image processing
(bio)medical applications

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Published Papers (13 papers)

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Editorial

Jump to: Research, Review

3 pages, 173 KiB

Open AccessEditorial

Special Issue on Advances in Intelligent Systems

by Zong Woo Geem, Seokwon Yeom, Euntai Kim, Myung-Geun Chun and Young-Jae Ryoo

Appl. Sci. 2023, 13(6), 3840; https://doi.org/10.3390/app13063840 - 17 Mar 2023

Viewed by 1233

Abstract

This Special Issue invites state-of-the-art research in intelligent systems [...] Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

Research

Jump to: Editorial, Review

14 pages, 871 KiB

Open AccessArticle

Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror

by Bo-Seung Yang, Tae-Won Kang, Yong-Sik Choi and Jin-Woo Jung

Appl. Sci. 2022, 12(14), 7024; https://doi.org/10.3390/app12147024 - 12 Jul 2022

Cited by 3 | Viewed by 1770

Abstract

As the elderly population increases globally, the demand for systems and algorithms that target the elderly is increasing. Focusing on the extendibility of smart mirrors, our purpose is to create a motion detection system based on video input by an attached device (an RGB camera). The motion detection system presented in this paper is based on an algorithm that returns a Boolean value indicating the detection of motion based on skeletal information. We analyzed the problems that occur when the adjacent frame subtraction method (AFSM) is used in the motion detection algorithm based on the skeleton-related output of the pose estimation model. We compared and tested the motion recognition rate for slow-motion with the previously used AFSM and the vector sum method (VSM) proposed in this paper. As an experimental result, the slow-motion detection rate showed an increase of 30–70%. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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9 pages, 3774 KiB

Open AccessArticle

Prediction of Minimum Night Flow for Enhancing Leakage Detection Capabilities in Water Distribution Networks

by Sang Soo Lee, Ho-Hyun Lee and Yun-Jung Lee

Appl. Sci. 2022, 12(13), 6467; https://doi.org/10.3390/app12136467 - 25 Jun 2022

Cited by 7 | Viewed by 2672

Abstract

In South Korea, a water supply enhancement project is being carried out to preemptively respond to drought and water loss by reducing pipeline leakages and supplying stable tap water through the maintenance of an aging water supply network. In order to reduce water leakage, a District Metered Area (DMA) was established to monitor and predict the minimum night flow based on flow data collected from IoT sensors. In this study, a model based on Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) was constructed to predict the MNF (minimum night flow) of County Y. The prediction of MNF results was compared with the MLP networks and the LSTM model. The outcome showed that the LSTM-MNF model proposed in this study performed better than the MLP-MNF model. Therefore, the research methods of this study can contribute to technical support for leakage reductions by preemptively responding to the expected increase in leakage through the prediction of the minimum flow at night. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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11 pages, 2603 KiB

Open AccessArticle

Improved Analytic Expansions in Hybrid A-Star Path Planning for Non-Holonomic Robots

by Chien Van Dang, Heungju Ahn, Doo Seok Lee and Sang C. Lee

Appl. Sci. 2022, 12(12), 5999; https://doi.org/10.3390/app12125999 - 13 Jun 2022

Cited by 27 | Viewed by 4562

Abstract

In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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12 pages, 1327 KiB

Open AccessArticle

Weighted Averaging Federated Learning Based on Example Forgetting Events in Label Imbalanced Non-IID

by Mannsoo Hong, Seok-Kyu Kang and Jee-Hyong Lee

Appl. Sci. 2022, 12(12), 5806; https://doi.org/10.3390/app12125806 - 7 Jun 2022

Cited by 8 | Viewed by 3366

Abstract

Federated learning, a data privacy-focused distributed learning method, trains a model by aggregating local knowledge from clients. Each client collects and utilizes its own local dataset to train a local model. Local models in the connected federated learning network are uploaded to the server. In the server, local models are aggregated into a global model. During the process, no local data is transmitted in or out of any client. This procedure may protect data privacy; however, federated learning has a worse case of example forgetting problem than centralized learning. The problem manifests in lower performance in testing. We propose federated weighted averaging (FedWAvg). FedWAvg identifies forgettable examples in each client and utilizes that information to rebalance local models via weighting. By weighting clients with more forgettable examples, such clients are better represented and global models can acquire more knowledge from normally neglected clients. FedWAvg diminishes the example forgetting problem and achieve better performance. Our experiments on SVHN and CIFAR-10 datasets demonstrate that our proposed method gets improved performance compared to existing federated learning algorithm in non-IID settings, and that our proposed method can palliate the example forgetting problem. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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24 pages, 1404 KiB

Open AccessArticle

Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks

by Huynh Cong Viet Ngu and Keon Myung Lee

Appl. Sci. 2022, 12(11), 5749; https://doi.org/10.3390/app12115749 - 6 Jun 2022

Cited by 12 | Viewed by 5285

Abstract

Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, the CNN–SNN conversion is considered one of the most successful approaches to training SNNs. However, previous works assume a rather long inference time period called inference latency to be allowed, while having a trade-off between inference latency and accuracy. One of the main reasons for this phenomenon stems from the difficulty in determining proper a firing threshold for spiking neurons. The threshold determination procedure is called a threshold balancing technique in the CNN–SNN conversion approach. This paper proposes a CNN–SNN conversion method with a new threshold balancing technique that obtains converted SNN models with good accuracy even with low latency. The proposed method organizes the SNN models with soft-reset IF spiking neurons. The threshold balancing technique estimates the thresholds for spiking neurons based on the maximum input current in a layerwise and channelwise manner. The experiment results have shown that our converted SNN models attain even higher accuracy than the corresponding trained CNN model for the MNIST dataset with low latency. In addition, for the Fashion-MNIST and CIFAR-10 datasets, our converted SNNs have shown less conversion loss than other methods in low latencies. The proposed method can be beneficial in deploying efficient SNN models for recognition tasks on resource-limited systems because the inference latency is strongly associated with energy consumption. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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16 pages, 8522 KiB

Open AccessArticle

Rough IPFCM Clustering Algorithm and Its Application on Smart Phones with Euclidean Distance

by Chih-Ming Chen, Sheng-Chieh Chang, Chen-Chia Chuang and Jin-Tsong Jeng

Appl. Sci. 2022, 12(10), 5195; https://doi.org/10.3390/app12105195 - 20 May 2022

Cited by 5 | Viewed by 1487

Abstract

New interval clustering technology for symbolic data analysis (SDA) on smart phones is shown to be beneficial for mobile computing devices for smart data analysis in this paper. A new interval clustering method that combined the rough set with interval possibilistic fuzzy C-means (IPFCM) algorithm under Euclidean distance is proposed and implemented on smart phones. Symbolic clustering algorithms (SCAs) have been widely used for pattern recognition, data mining, artificial intelligence, etc. In general, the SCA is unsupervised classification that is divided into groups according to symbolic data sets. However, the traditional interval fuzzy C-means (IFCM) clustering method still has noisy and data overlapping problems associated with these symbolic interval data. Hence, a new rough set with the interval possibilistic fuzzy C-means (RIPFCM) clustering algorithm with Euclidean distance was proposed to address the symbolic interval data (SID). That is, the proposed method can perform better than the traditional IFCM clustering algorithm for SID clustering in noisy environments and with data overlapping problems. The new RIPFCM algorithm under the Euclidean distance method was proposed to deal with SID on new applications in smart phones. Consequently, this method shows the expansion of the smart phone’s computing power and its future application in new SDA. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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13 pages, 7806 KiB

Open AccessArticle

ROS-Based Unmanned Mobile Robot Platform for Agriculture

by Eu-Tteum Baek and Dae-Yeong Im

Appl. Sci. 2022, 12(9), 4335; https://doi.org/10.3390/app12094335 - 25 Apr 2022

Cited by 21 | Viewed by 5541

Abstract

While the demand for new high-tech technologies is rapidly increasing, difficulties are presented, such as aging and population decline in rural areas. In particular, autonomous mobile robots have been emerging in the agricultural field. Worldwide, huge investment is being made in the development of unmanned agricultural mobile robots; meanwhile with the development of robots, modern farms have high expectations of increased productivity. However, in the agricultural work environment, it is difficult to solve these problems with the existing mobile robot form, due to the difficulties of various environments. Typical problems are space constraints in the agricultural work environment, the high computational complexity of algorithms, and changes in the environment. To solve these problems, in this paper, we propose a method to design and operate a mobile robot platform that can be used in a greenhouse. We represent a robot type with two drive wheels along with four casters that can operate on path and rail. In addition, we propose a technology for a multi-AI deep learning system to operate a robot, an algorithm that can operate such a robot, and a VPN-based communication system for network and security. The proposed method is expected to increase productivity and reduce labor costs in the agricultural work environment. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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16 pages, 1873 KiB

Open AccessArticle

Logit Averaging: Capturing Global Relation for Session-Based Recommendation

by Heeyoon Yang, Gahyung Kim and Jee-Hyoung Lee

Appl. Sci. 2022, 12(9), 4256; https://doi.org/10.3390/app12094256 - 22 Apr 2022

Cited by 4 | Viewed by 2028

Abstract

Session-based recommendation predicts an anonymous user’s next action, whether she or he is likely to purchase based on the user’s behavior in the current session as sequences. Most recent research on session-based recommendations makes predictions based on a single-session without incorporating global relationships between sessions. It does not guarantee a better performance because item embeddings learned by solely utilizing a single session (inter-session) have less item transition information than utilizing both intra- and inter-session ones. Some existing methods tried to enhance recommendation performance by adopting memory modules and global transition graphs; however, those need more computation cost and time. We propose a novel algorithm called Logit Averaging (LA), utilizing both (i) local-level logits, which come from intra-sessions item transitions and (ii) global-level logits, which come from gathered logits of related sessions. The proposed method shows an improvement in recommendation performance in respect of accuracy and diversity through extensive experiments. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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18 pages, 5665 KiB

Open AccessArticle

Development of Priority Index for Intelligent Vessel Traffic Monitoring System in Vessel Traffic Service Areas

by Lee-na Lee and Joo-sung Kim

Appl. Sci. 2022, 12(8), 3807; https://doi.org/10.3390/app12083807 - 9 Apr 2022

Cited by 6 | Viewed by 2503

Abstract

Recognizing dangerous situations in advance and determining priority is essential in vessel traffic surveillance. The traffic management priority is determined by the vessel traffic service operator (VTSO) employing the closest point of approach (CPA) and the time to CPA (TCPA) of the targets considering their current navigational data. Various environmental conditions influence CPA and TCPA, which affects the importance of surveillance. This study aims to support vessel traffic prioritization in the navigation surveillance of VTSO from the observer side. The vessel tracks were clustered based on density, and a priority index of the vessel surveillance was developed in the VTS area by reflecting regional navigation characteristics. Density-based spatial clustering of applications with noise (DBSCAN) was used for data clustering to classify the surveillance area. A fuzzy membership function was constructed based on the CPA and TCPA belonging to each cluster, and a dataset for determining priorities was constructed, yielding 17 clusters, fuzzy rules, and tables, with the priority index extracted for all vessel pairs to visualize the priority. The results indicated prior recognition of all dangerous situations. The proposed method facilitates vessel surveillance priority determination in high-density areas and predicts the risk in advance, thereby contributing to traffic management. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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20 pages, 8033 KiB

Open AccessArticle

Growing Neural Gas with Different Topologies for 3D Space Perception

by Yuichiro Toda, Akimasa Wada, Hikari Miyase, Koki Ozasa, Takayuki Matsuno and Mamoru Minami

Appl. Sci. 2022, 12(3), 1705; https://doi.org/10.3390/app12031705 - 7 Feb 2022

Cited by 12 | Viewed by 2891

Abstract

Three-dimensional space perception is one of the most important capabilities for an autonomous mobile robot in order to operate a task in an unknown environment adaptively since the autonomous robot needs to detect the target object and estimate the 3D pose of the target object for performing given tasks efficiently. After the 3D point cloud is measured by an RGB-D camera, the autonomous robot needs to reconstruct a structure from the 3D point cloud with color information according to the given tasks since the point cloud is unstructured data. For reconstructing the unstructured point cloud, growing neural gas (GNG) based methods have been utilized in many research studies since GNG can learn the data distribution of the point cloud appropriately. However, the conventional GNG based methods have unsolved problems about the scalability and multi-viewpoint clustering. In this paper, therefore, we propose growing neural gas with different topologies (GNG-DT) as a new topological structure learning method for solving the problems. GNG-DT has multiple topologies of each property, while the conventional GNG method has a single topology of the input vector. In addition, the distance measurement in the winner node selection uses only the position information for preserving the environmental space of the point cloud. Next, we show several experimental results of the proposed method using simulation and RGB-D datasets measured by Kinect. In these experiments, we verified that our proposed method almost outperforms the other methods from the viewpoint of the quantization and clustering errors. Finally, we summarize our proposed method and discuss the future direction on this research. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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20 pages, 5054 KiB

Open AccessArticle

Patent Analysis Using Bayesian Data Analysis and Network Modeling

by Sangsung Park and Sunghae Jun

Appl. Sci. 2022, 12(3), 1423; https://doi.org/10.3390/app12031423 - 28 Jan 2022

Cited by 10 | Viewed by 4233

Abstract

Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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Review

Jump to: Editorial, Research

7 pages, 668 KiB

Open AccessReview

Studies to Overcome Brain–Computer Interface Challenges

by Woo-Sung Choi and Hong-Gi Yeom

Appl. Sci. 2022, 12(5), 2598; https://doi.org/10.3390/app12052598 - 2 Mar 2022

Cited by 12 | Viewed by 4064

Abstract

A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain’s functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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